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Demographic inference using genetic data from a single individual: Separating population size variation from

Olivier Mazet1, Willy Rodríguez1, Lounès Chikhi2

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New methods can now estimate population genetics parameters and choose between demographic models using genetic data from a single individual. This advances our ability to reconstruct population history despite challenges from population structure.

Keywords:
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Area of Science:

  • Population genetics
  • Genomics
  • Evolutionary biology

Background:

  • Advancements in sequencing technologies offer unprecedented opportunities for population genetics research.
  • Genomic data aids in reconstructing population history but presents challenges due to population structure, which can mimic demographic events like bottlenecks.

Purpose of the Study:

  • To develop and validate new inferential methods for population genetics that account for population structure.
  • To enable accurate estimation of demographic parameters and efficient model choice using genomic data.

Main Methods:

  • Re-derivation of coalescence time distributions for two demographic models (instantaneous change and island model).
  • Application of maximum likelihood estimation for model parameters.
  • Validation of estimation and model selection procedures (Kolmogorov-Smirnov test, AIC) using simulations.
  • Derivation of the distribution for differences between non-recombining sequences.

Main Results:

  • Demonstrated the feasibility of estimating demographic parameters under different models.
  • Showcased efficient model choice capabilities using genetic data from a single diploid individual.
  • Validated the robustness of the estimation and model selection procedures across various parameter combinations.

Conclusions:

  • The developed methods allow for reliable parameter estimation and model selection in population genetics.
  • These approaches effectively address challenges posed by population structure, enhancing the reconstruction of demographic histories.
  • Genomic data from a single individual can be sufficient for sophisticated population genetic inferences.